Detecting walking gait impairment with an ear-worn sensor

This paper investigates an ear worn sensor for the development of a gait analysis framework. Instead of explicitly defining gait features that indicate injury or impairment, an automatic method of feature extraction and selection is proposed. The proposed framework uses multi-resolution wavelet analysis and margin based feature selection. It was validated on three datasets; the first simulating a leg injury, the second simulating abdominal impairment that could result from surgery or injury and the third is a dataset collected from a patient during recovery from leg injury. The method shows a clear distinction of gait between injured and normal walking. It also illustrates the fact that using source separation before pattern classification can significantly improve the proposed gait analysis framework.